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Found 40 papers, 17 papers with code
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
Polar codes, developed on the foundation of Arikan's polarization kernel, represent a breakthrough in coding theory and have emerged as the state-of-the-art error-correction-code in short-to-medium block length regimes.
ZeroSwap: Data-driven Optimal Market Making in DeFi
This ensures that the market maker balances losses to informed traders with profits from noise traders.
Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes
Next, we derive the soft-decision based version of our algorithm, called soft-subRPA, that not only improves upon the performance of subRPA but also enables a differentiable decoding algorithm.
CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family
We design a principled curriculum, guided by information-theoretic insights, to train CRISP and show that it outperforms the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(32, 16) and Polar(64, 22) codes.
TinyTurbo: Efficient Turbo Decoders on Edge
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO .
KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning
In this paper, we construct KO codes, a computationaly efficient family of deep-learning driven (encoder, decoder) pairs that outperform the state-of-the-art reliability performance on the standardized AWGN channel.
Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding
To lower the complexity of our decoding algorithm, referred to as subRPA in this paper, we investigate different ways for pruning the projections.
Information Theory Information Theory
Enriching Word Embeddings with Temporal and Spatial Information
The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes.
Deepcode and Modulo-SK are Designed for Different Settings
DeepCode is designed and evaluated for the AWGN channel with (potentially delayed) uncoded output feedback.
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications.
Practical Low Latency Proof of Work Consensus
Bitcoin is the first fully-decentralized permissionless blockchain protocol to achieve a high level of security, but at the expense of poor throughput and latency.
Distributed, Parallel, and Cluster Computing Cryptography and Security Networking and Internet Architecture
Learning in Gated Neural Networks
Gating is a key feature in modern neural networks including LSTMs, GRUs and sparsely-gated deep neural networks.
DeepTurbo: Deep Turbo Decoder
We focus on Turbo codes and propose DeepTurbo, a novel deep learning based architecture for Turbo decoding.
Context-Sensitive Malicious Spelling Error Correction
Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection.
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards.
Learning One-hidden-layer Neural Networks under General Input Distributions
Significant advances have been made recently on training neural networks, where the main challenge is in solving an optimization problem with abundant critical points.
Preposition Sense Disambiguation and Representation
Prepositions are highly polysemous, and their variegated senses encode significant semantic information.
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
In particular, we propose PolyShard: ``polynomially coded sharding'' scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system.
Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Information Theory
Deepcode: Feedback Codes via Deep Learning
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications.
Embedding Syntax and Semantics of Prepositions via Tensor Decomposition
Prepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences.
Communication Algorithms via Deep Learning
We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
Once the experts are known, the recovery of gating parameters still requires an EM algorithm; however, we show that the EM algorithm for this simplified problem, unlike the joint EM algorithm, converges to the true parameters.
Graph2Seq: Scalable Learning Dynamics for Graphs
By not limiting the representation to a fixed dimension, Graph2Seq scales naturally to graphs of arbitrary sizes and shapes.
Deanonymization in the Bitcoin P2P Network
Recent attacks on Bitcoin's peer-to-peer (P2P) network demonstrated that its transaction-flooding protocols, which are used to ensure network consistency, may enable user deanonymization---the linkage of a user's IP address with her pseudonym in the Bitcoin network.
Estimating Mutual Information for Discrete-Continuous Mixtures
We provide numerical experiments suggesting superiority of the proposed estimator compared to other heuristics of adding small continuous noise to all the samples and applying standard estimators tailored for purely continuous variables, and quantizing the samples and applying standard estimators tailored for purely discrete variables.
Discovering Potential Correlations via Hypercontractivity
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest.
Representing Sentences as Low-Rank Subspaces
Sentences are important semantic units of natural language.
MORSE: Semantic-ally Drive-n MORpheme SEgment-er
We present in this paper a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes.
Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology
We present an unsupervised and language-agnostic method for learning root-and-pattern morphology in Semitic languages.
Prepositions in Context
Prepositions are highly polysemous, and their variegated senses encode significant semantic information.
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and { text classification}) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.
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Dandelion: Redesigning the Bitcoin Network for Anonymity
We propose a simple networking policy called Dandelion, which achieves nearly-optimal anonymity guarantees at minimal cost to the network's utility.
Cryptography and Security Information Theory Information Theory
Geometry of Compositionality
This paper proposes a simple test for compositionality (i. e., literal usage) of a word or phrase in a context-specific way.
Geometry of Polysemy
Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec.
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
In this paper, we combine both these approaches to design new estimators of entropy and mutual information that outperform state of the art methods.
Demystifying Fixed k-Nearest Neighbor Information Estimators
In this paper we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the bias as a function of number of samples.
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables.
Secure Multi-party Differential Privacy
In this setting, each party is interested in computing a function on its private bit and all the other parties' bits.
Spy vs. Spy: Rumor Source Obfuscation
Whether for fear of judgment or personal endangerment, it is crucial to keep anonymous the identity of the user who initially posted a sensitive message.
The Composition Theorem for Differential Privacy
Sequential querying of differentially private mechanisms degrades the overall privacy level.
Data Structures and Algorithms Cryptography and Security Information Theory Information Theory
